1. Field of the Invention
The present invention relates to a technology of determining the level of the effect of each one of a plurality of entities constructing a system on the performance thereof, wherein the status of each entity changes with time.
2. Description of the Related Art
In many systems, it is of interest to control the throughput of each system, usually in order to maximize the throughput. For example, in a manufacturing system, it may be of interest to maximize the number of products or parts produced within a certain time.
The throughput of a real system, however, is always finite. While many factors in a system affect the throughput, it is usually only a few entities (e.g., processing entities such as machines, transporting devices, processors of a computer, etc.) in a system limiting the throughput.
These limiting entities in a system are commonly called bottlenecks or constraints. These bottlenecks or constraints limit the overall flow of a discrete event system constructed with entities. Each entity constructing a system is a material, a human, or an abstract element, as for example as a machine, a worker, an order, information, etc.
Subsequently, in order to change the system throughput, it is necessary to change the throughput of the one or more bottlenecks. Adjusting non-bottleneck entities will usually have little or no effect on the throughput.
Consequently, it is important to determine the effect-level at which each entity constructing a system affects the performance or throughput of the system, and to detect, on the basis of the thus determined effect-level, one or more entities of the system as one or more bottlenecks.
Currently, a number of different methods for detecting the bottleneck are in use. One commonly used method is to determine the busiest entity, i.e., the entity that has the largest percentage working time, and eventually has the smallest percentage idle time.
In this conventional method, as illustrated with a flow chart in FIG. 27, step S101 is first implemented to collect data required for determining the bottleneck of a system, and step S102 is then implemented to measure the percentage working time of each one of entities constructing the system. Step S103 is thereafter implemented to order the entities by the measured percentage working time, and step S104 is then implemented to determine that one or more entities with the largest percentage working time is one or more bottlenecks of the system.